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Weakly supervised deep learning to predict recurrence in low-grade endometrial cancer from multiplexed immunofluorescence images.
- Source :
-
NPJ digital medicine [NPJ Digit Med] 2023 Mar 23; Vol. 6 (1), pp. 48. Date of Electronic Publication: 2023 Mar 23. - Publication Year :
- 2023
-
Abstract
- Predicting recurrence in low-grade, early-stage endometrial cancer (EC) is both challenging and clinically relevant. We present a weakly-supervised deep learning framework, NaroNet, that can learn, without manual expert annotation, the complex tumor-immune interrelations at three levels: local phenotypes, cellular neighborhoods, and tissue areas. It uses multiplexed immunofluorescence for the simultaneous visualization and quantification of CD68 + macrophages, CD8 + T cells, FOXP3 + regulatory T cells, PD-L1/PD-1 protein expression, and tumor cells. We used 489 tumor cores from 250 patients to train a multilevel deep-learning model to predict tumor recurrence. Using a tenfold cross-validation strategy, our model achieved an area under the curve of 0.90 with a 95% confidence interval of 0.83-0.95. Our model predictions resulted in concordance for 96,8% of cases (κ = 0.88). This method could accurately assess the risk of recurrence in EC, outperforming current prognostic factors, including molecular subtyping.<br /> (© 2023. The Author(s).)
Details
- Language :
- English
- ISSN :
- 2398-6352
- Volume :
- 6
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- NPJ digital medicine
- Publication Type :
- Academic Journal
- Accession number :
- 36959234
- Full Text :
- https://doi.org/10.1038/s41746-023-00795-x